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- Publisher Website: 10.1111/j.1937-5956.2011.01261.x
- Scopus: eid_2-s2.0-84859012387
- WOS: WOS:000301648400006
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Article: Inventory policy with parametric demand: Operational statistics, linear correction, and regression
Title | Inventory policy with parametric demand: Operational statistics, linear correction, and regression |
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Authors | |
Keywords | demand ambiguity newsvendor model model uncertainty operational statistics |
Issue Date | 2012 |
Citation | Production and Operations Management, 2012, v. 21, n. 2, p. 291-308 How to Cite? |
Abstract | In this paper, we consider data-driven approaches to the problem of inventory control. We first consider the approach of operational statistics and review related results which enable us to maximize a priori expected profit uniformly over all parameter values, when the demand distribution is known up to the location and scale parameters. For the case of the unknown shape parameter, we first suggest a heuristic approach based on operational statistics to obtain improved ordering policies and illustrate the same for the case of a Pareto demand distribution. In more general cases where the heuristic is not applicable, we suggest linear correction and support vector regression approaches to better estimate ordering policies, and illustrate these using a Gamma demand distribution. In certain cases, our proposed approaches are found to yield significant improvements. © 2011 Production and Operations Management Society. |
Persistent Identifier | http://hdl.handle.net/10722/296243 |
ISSN | 2021 Impact Factor: 4.638 2020 SCImago Journal Rankings: 3.279 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Ramamurthy, Vivek | - |
dc.contributor.author | George Shanthikumar, J. | - |
dc.contributor.author | Shen, Zuo Jun Max | - |
dc.date.accessioned | 2021-02-11T04:53:08Z | - |
dc.date.available | 2021-02-11T04:53:08Z | - |
dc.date.issued | 2012 | - |
dc.identifier.citation | Production and Operations Management, 2012, v. 21, n. 2, p. 291-308 | - |
dc.identifier.issn | 1059-1478 | - |
dc.identifier.uri | http://hdl.handle.net/10722/296243 | - |
dc.description.abstract | In this paper, we consider data-driven approaches to the problem of inventory control. We first consider the approach of operational statistics and review related results which enable us to maximize a priori expected profit uniformly over all parameter values, when the demand distribution is known up to the location and scale parameters. For the case of the unknown shape parameter, we first suggest a heuristic approach based on operational statistics to obtain improved ordering policies and illustrate the same for the case of a Pareto demand distribution. In more general cases where the heuristic is not applicable, we suggest linear correction and support vector regression approaches to better estimate ordering policies, and illustrate these using a Gamma demand distribution. In certain cases, our proposed approaches are found to yield significant improvements. © 2011 Production and Operations Management Society. | - |
dc.language | eng | - |
dc.relation.ispartof | Production and Operations Management | - |
dc.subject | demand ambiguity | - |
dc.subject | newsvendor model | - |
dc.subject | model uncertainty | - |
dc.subject | operational statistics | - |
dc.title | Inventory policy with parametric demand: Operational statistics, linear correction, and regression | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1111/j.1937-5956.2011.01261.x | - |
dc.identifier.scopus | eid_2-s2.0-84859012387 | - |
dc.identifier.volume | 21 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 291 | - |
dc.identifier.epage | 308 | - |
dc.identifier.eissn | 1937-5956 | - |
dc.identifier.isi | WOS:000301648400006 | - |
dc.identifier.issnl | 1059-1478 | - |